Images and image analysis are increasingly being used for autonomous vehicle control and simulation. An important aspect of this task is to identify objects in sets of received images or video feeds. The identification and location of objects in the images can be useful for configuring the control or simulation of a vehicle. However, it can be very difficult to automate the process of image analysis and object labeling. Variations in image quality, environmental conditions, and processing capabilities can hinder the image analysis and object labeling process. Consequently, it can be difficult for autonomous vehicle control and simulation systems to perform effectively.
One approach for image analysis and object labeling is to annotate objects in the images with one or more labels. The labeled objects can then be classified and further processed to determine location and velocity. This image and object annotation can be performed manually by people that view each image. However, conventional manual approaches to annotating images are time-consuming, financially untenable, and prone to inconsistencies resulting from viewers' subjectivities. Automated approaches have also been developed. These automated approaches can be significantly more efficient than manual ones and can be scaled accordingly. Unfortunately, current automated approaches to annotating images produce many mislabeled objects.